Artificial Intelligence

Curriculum

Module 01 – Introduction of Artificial Intelligence

• Introduction of Artificial Intelligence
• Terminologies of Artificial Intelligence
• Components of Artificial Intelligence – ML & DL
• Difference between AI, ML, Deep Learning
• Introduction to Machine Learning
• History and Evolution of AI
• Find out where AI is applied in Technology and Science.
• Difference between Traditional Programming and ML Programming

Module 02 – Steps of AI/ML Implementations

• Types of Machine Learning
• Labelled Data and Unlabeled Data
• Concept of Supervised Machine Learning
• Concept of Unsupervised Machine Learning
• Steps of Machine Learning
• Concept of Collecting the historic training Data for ML
• Concept of Preprocess data for Machine Learning
• Concept of Train the ML model
• Concept of Test the ML Algorithm
• Concept of using the ML Algorithm

Module 03 – Data Collection for Machine Learning

• Introduction
• Types of Data collection- Offline Data and Online Data
• Practical implementations of Reading the offline dataset using Numpy
• Practical implementations of Reading the online dataset using Numpy
• Practical implementations of Reading the offline dataset using Pandas
• Practical implementations of Reading the online iris dataset using Pandas

Module 04 – Concept of Supervise & Unsupervised Machine Learning

• Introduction
• Types of Machine Learning
• Labelled Data and Unlabeled Data
• Concept of Supervised Machine Learning
• Concept of Unsupervised Machine Learning
• Regression and Classification
• Linear Regression and Logistic Regression

Module 05 – Data Visualization for Machine Learning using Matplotlib

• Introduction
• Concept of Univariate plots
• Univariate Histogram Plots.
• Univariate Density Plots.
• Univariate Box and Whisker Plots.
• Concept of Multivariate plots
• Correlation Matrix Plot
• Scatter Matrix Plot

Module 06 – Practical implementation of Supervised ML Algorithm

• Introduction
• Implementation Foundation of Supervised Machine Learning Algorithms
• Regression and Classification
• Linear Regression and Logistic Regression
• Practical implementations of Supervised ML Algorithms- Linear Regression
• Practical implementations of Supervised ML Algorithms- Logistic Regression
• Concept of Sigmoid Function
• k-NN Algorithm
• Naive Bayes Classifiers
• Decision trees etc.

Module 07 – Practical implementation of Unsupervised Machine Learning 

• Introduction
• Concepts and Steps of Unsupervised Machine Learning Algorithm 
• Concept of Clustering,
• Practical implementations of Machine Learning Unsupervised Algorithms
• K-Means Clustering.

Module 08 – Prepare Data for ML using Data Transformation Methods

• Introduction
• Need for Data Pre-processing
• Data Transforms Steps
• Types of Data Transformation Methods
• Rescale Data
• Standardize Data
• Normalize Data
• Binarize Data

Module 09 – Feature Selection for Machine Learning

• Introduction
• Feature Selection
• Univariate Feature Selection
• Recursive Feature Elimination
• Principal Component Analysis
• Feature Selection based on Importance.

Module 10 – Data Resampling Methods for Evaluation of ML Models

• Introduction
• Evaluate Machine Learning Algorithms
• Split into Train and Test Sets
• K-fold Cross Validation
• Leave One Out Cross Validation
• Repeated Random Test-Train Splits
• What Techniques to Use When

Module 11 – Machine Learning Algorithm Performance Evaluation Metrics

• Introduction
• Algorithm Evaluation Metrics
• Logistic Regression Algorithm Performance Evaluation Metrics
• Classification Accuracy (Default).
• Logarithmic Loss.
• Area Under ROC Curve (AUC).
• Confusion Matrix.
• Classification Report.
• Linear Regression Algorithm Performance Evaluation Metrics
• Mean Absolute Error.
• Mean Squared Error.
• R2 Error

Module 12 – Spot-Check Machine Learning Algorithms

• Concept of Algorithm Spot-Checking
• Algorithms Overview
• Linear Machine Learning Algorithms Spot-check
• Nonlinear Machine Learning Algorithms Spot-check

Module 13 – Introduction to Deep Learning

• A revolution in Artificial Intelligence
• Limitations of Machine Learning
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning

Module 14 – Introduction to Neural Networks

• How Deep Learning Works?
• Introduction to Neural Networks
• Neural Network Architecture
• The Neuron
• Training a Perceptron
• Concept of Gradient Descent
• Stochastic Gradient Descent (SDG)
• Activation Functions
• Neural Network Layers

Module 15 – Deep dive into ANN with Tensor Flow

• Understand limitations of a Single Perceptron
• Deepening the network
• Tensor Flow code-basics
• Tensor flow data types
• CPU vs GPU vs TPU
• Tensor flow methods
• Overfitting and Regularization
• Debugging Neural Networks
• Visualizing NN using Tensor Flow
• The MNIST Dataset
• Coding MNIST NN
• Linear Regression example revisited.
• Generalization, Overfitting, Under fitting

Module 16 – Computer Vision 

• Introduction to image processing and computer vision
• Convolutional features for visual recognition
• Object detection
• Image classification

Module 17 – Introduction of Convolutional Neural Networks (CNN)

• Introduction
• Images and Pixels
• How humans recognize images
• Convolutional Neural Networks

Module 18 – Architecture of Convolutional Neural Networks (CNN)

• ConvNet Architecture
• Strides and Zero Padding
• Max Pooling and ReLU activations
• Dropout
• Coding Deep ConvNets demo

Module 19 – Keras API

• Keras API
• How to compose Models using Keras
• Sequential Composition
• Neural Network Layers with Keras & Tensor Flow

Module 20 – Conclusion and Future of Artificial Intelligence

• Summary of Artificial Intelligence concepts and techniques
• Artificial Intelligence trends and future developments
• Artificial Intelligence and society
• Artificial Intelligence ethics and safety.

Module 21: Capstone Project

• Participants will work on a project involving AI algorithms, such as image classification using CNN.
• The project will demonstrate the application of AI techniques learned during the course.